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CASE (Cognitive Agent for Social Environments) Department of Computer Science Department of Sociology/Urban Studies Trinity University. Outline. Cognitive Agents Micro-macro Level Interaction System Architecture Decision Models Sugarscape. Cognitive Agents.

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CASE

(Cognitive Agent for Social Environments)

Department of Computer Science

Department of Sociology/Urban Studies

Trinity University


Outline
Outline

  • Cognitive Agents

  • Micro-macro Level Interaction

  • System Architecture

  • Decision Models

  • Sugarscape


Cognitive agents
Cognitive Agents

  • They have beliefs about the state of the environment.

  • They have knowledge about actions and plans of actions.

  • They have knowledge about how their actions will affect the environment and other agents.

  • They have explicitly goals

  • They are capable of reasoning about how to achieve goals (also known as intentional or deliberative agents).

  • They communicate using Agent Communication Language (KIF and KQML).


Social iden

tity

Society

Bounded rationality

Social proof

Following intuition

or

Social

approva

l

d

eliberative reasoning

Social Laws &

Emergence

Constraints

of properties

Agents

Perceptions

·

Communication

Actions

·

Constraints

·

Environment

Sphere of

Influence

Macro-Micro Level Interaction


Social laws and constraints
Social Laws and Constraints

  • Social identity. It includes an individual’s age, gender, social positions and religions.

  • Social proof. It is a phenomenon that when people encounter a new situation with insufficient information, he is more likely to follow the decisions made by others, special the people from the same society as him. People are adept at adopting one others’ innovations because early decisions by group members change the environment and hence, the attractiveness of choices for subsequent group members, i.e., early group members reduce the costs for followers.

  • Social approval. People desire to obtain social approval from others. Others may know something that they don’t. Therefore, getting social approvals can help one share the information that others know.


Two phases in a decision process
Two Phases in a Decision Process

  • Tverskey and Kahneman

    • Editing: the agent constructs a representation of the acts, contingencies and outcomes that are relevant to the decision.

      • Framing: the agent frame an outcome or transaction in its mind and the utility it expects to receive.

      • Anchoring: the agent’s tendency to overly or heavily rely on one trait or piece of information when making decisions.

      • Accessibility: the importance of a fact within the selective attention.

    • Evaluation: the agent assesses the value of each alternative and chooses the alternative of highest value.

      • Two mode systems: intuition and deliberation.

      • Satisfying theory: being good enough.


System architecture

Editing

Agent

Framing Anchoring Accessibility

.

.

.

KB

Social Laws &

Constraints

Tone of decision-making

Evaluation

Perception

System Architecture

Two Modes of

Function

Intuition

Deliberation

communication

Alternatives

Satisficing Decision-Making

message

action

Visualization Interface

Environment


Agent execution function
Agent Execution Function

/*The function is executed independently by each agent, denoted self below.*/

function update(KBself, env, messageQueue, t)

inputs: KBself, the knowledge base for agent self

env, the environment

messageQueue, the message queue for self

t, the current step

//editing phase

observation(env);

check(messageQueue);

editing(KBself);

//evaluation phase

action = evaluate(KBself);

message = evaluate(KBself);

//performing the outputs of the evaluation phase

do(action);

resource-sych(env);

update(env);

add(message, messageQueue);

masterserver-sych;

// move to next step

t++;


Decision models
Decision Models

  • Normative model: what people should ideally do.

    • The attractiveness (or utility) of possible consequence of the alternative (i.e. aspects).

    • The probability of each consequence.

    • e.g. MEU

  • Descriptive model: what people do

    • e.g. MADM

  • Perspective model: what people should do


Multiple attribute decision making
Multiple Attribute Decision-Making

  • MADM refers to making preference decisions over the available alternatives that are characterized by multiple, usually conflicting, attributes.

    • Alternatives

    • Attributes

    • Attribute Weights

    • Decision Matrix

      • Xij indicating the performance rating of the ith alternative with respect to the jth attribute.


An example budget reduction decision
An Example: Budget Reduction Decision

  • In 1988, a significant budget reduction at the University of Wyoming left the Athletic Department nearly $700,000 short on operating funds.

qualitative

quantitative

Heterogeneous data type


Process
Process

  • Attribute generation

    • Derive the attributes hierarchically from a super goal.

      • Complete and exhaustive: all important attributes

      • Mutually exclusive

  • Attribute weighting

    • w=(w1, …wj…, wn) s.t. wj=1

  • Quantification of qualitative ratings

  • Normalization of attribute ratings


Methods
Methods

  • Non-compensatory rules: trade-offs among attribute values are not permitted.

    • Simplicity.

    • Do not always yield a unique solution.

    • Using these rules implies a risk of neglecting important information.

  • Compensatory rules: otherwise.

    • They can, theoretically, be used in all situations.

    • Complex value judgments

      • They require comparisons of attractiveness values across different attributes whereas the non-compensatory rules only require comparisons within an attribute.

    • The overview problem

      • The decisions may be difficult to justify when there is a large amount of attributes being taken into account.

    • Lack of concreteness

      • The overall attractiveness measures tell us little about the underlying pattern of attractiveness values.

    • The give up problem.

      • Compensatory rules emphasize that one has to give up certain good things to get other good things and people hate the thought of giving up anything.


Examples of decision rules
Examples of Decision Rules

Dominance Satisfying Sequential Elimination Attitude Oriented


Preference ordering
Preference ordering

  • Ordinal relationship: which is superior to which

    • Preference

      • Attributes are preferentially independent of others

      • Attributes are preferentially dependent of others

  • Cardinal relationship: how much

    • Utility


Cp nets
CP-Nets

  • A CP-net is weak (only representing preference).

  • Richer models

    • TCP : tradeoff CP (adding importance to CP-nets).

    • UCP: utility CP (adding utility to CP-nets).

    • Uncertainty (probability)? Still research now.

    • Or mixing them all.

!ab!c

a>!a

A

!abc

a b>!b

!a !b>b

B

!a!b!c

!a!bc

b c>!c

!b !c>c

a!b!c

a!bc

C

ab!c

abc


Tcp nets
TCP-Nets

A

a > !a

unconditional importance relation

E

e > !e

a b > !b

!a !b > b

B

b d > !d

!b !d > d

b c > !c

!b !c > c

D

C

conditional importance relation

be C > D

!be D > C

b!e D > C


Ucp nets
UCP-Nets

b > !b

a !a

5 2

b !b

5 2

a > !a

A

B

A

B

ab c > !c

a!b !c > c

!ab !c > c

!a!b c > !c

c > !c

ab .6 .1

a!b .2 .8

!ab .1 .8

!a!b .9 .3

C

C

D

D

c d > !d

!c !d > d

d !d

c .9 .8

!c .2 .3

u(a, b, !c, !d) = f1(a) + f2(b) + f3(a, b, !c) + f4(!c, !d)

= 5 + 5 + .1 + .3

= 10.4


Sugarscape
Sugarscape

  • Simple agents in a simple landscape create an economy.

    • Heterogeneous agents + physical landscape

      • Emergence of groups.

      • The rich get richer.

    • 1 + Random Genetic endowments

      • A skewed wealth distribution.

    • 1 + 2 + sex

      • Least fit members died off; the most fit members had more and more offspring.

      • Population swings.


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